Online Class-Incremental (OCI) learning has sparked new approaches to expand the previously trained model knowledge from sequentially arriving data streams with new classes. Unfortunately, OCI learning can suffer from catastrophic forgetting (CF) as the decision boundaries for old classes can become inaccurate when perturbated by new ones. Existing literature have applied the data augmentation (DA) to alleviate the model forgetting, while the role of DA in OCI has not been well understood so far. In this paper, we theoretically show that augmented samples with lower correlation to the original data are more effective in preventing forgetting. However, aggressive augmentation may also reduce the consistency between data and corresponding labels, which motivates us to exploit proper DA to boost the OCI performance and prevent the CF problem. We propose the Enhanced Mixup (EnMix) method that mixes the augmented samples and their labels simultaneously, which is shown to enhance the sample diversity while maintaining strong consistency with corresponding labels. Further, to solve the class imbalance problem, we design an Adaptive Mixup (AdpMix) method to calibrate the decision boundaries by mixing samples from both old and new classes and dynamically adjusting the label mixing ratio. Our approach is demonstrated to be effective on several benchmark datasets through extensive experiments, and it is shown to be compatible with other replay-based techniques.

翻译：在线类增量（OCI）学习催生了新方法，旨在利用包含新类别的连续数据流扩展先前训练模型的知识。然而，由于旧类别的决策边界可能因新类别的扰动而失准，OCI学习可能遭受灾难性遗忘（CF）。现有文献已应用数据增强（DA）来缓解模型遗忘，但DA在OCI中的作用至今尚未得到充分理解。本文从理论上证明，与原始数据相关性较低的增强样本在防止遗忘方面更为有效。然而，激进的增强也可能降低数据与对应标签之间的一致性，这促使我们探索利用适当的DA来提升OCI性能并防止CF问题。我们提出增强混合（EnMix）方法，该方法同时混合增强样本及其标签，被证明能在增强样本多样性的同时，保持与对应标签的强一致性。此外，为解决类别不平衡问题，我们设计自适应混合（AdpMix）方法，通过混合新旧类别的样本并动态调整标签混合比例来校准决策边界。通过大量实验，我们的方法在多个基准数据集上被证明有效，且与其它基于回放的技术兼容。